Showing papers on "Vehicle dynamics published in 2019"
TL;DR: A decentralized adaptive formation controller is designed that ensures uniformly ultimate boundedness of the closed-loop system with prescribed performance and avoids collision between each vehicle and its leader.
Abstract: This paper addresses a decentralized leader–follower formation control problem for a group of fully actuated unmanned surface vehicles with prescribed performance and collision avoidance. The vehicles are subject to time-varying external disturbances, and the vehicle dynamics include both parametric uncertainties and uncertain nonlinear functions. The control objective is to make each vehicle follow its reference trajectory and avoid collision between each vehicle and its leader. We consider prescribed performance constraints, including transient and steady-state performance constraints, on formation tracking errors. In the kinematic design, we introduce the dynamic surface control technique to avoid the use of vehicle's acceleration. To compensate for the uncertainties and disturbances, we apply an adaptive control technique to estimate the uncertain parameters including the upper bounds of the disturbances and present neural network approximators to estimate uncertain nonlinear dynamics. Consequently, we design a decentralized adaptive formation controller that ensures uniformly ultimate boundedness of the closed-loop system with prescribed performance and avoids collision between each vehicle and its leader. Simulation results illustrate the effectiveness of the decentralized formation controller.
273 citations
TL;DR: Test results show that an automated vehicle with optimized plant and controller can perform its tasks well under aggressive, moderate, and conservative driving styles, further improving the overall performance.
Abstract: This paper studies the codesign optimization approach to determine how to optimally adapt automatic control of an intelligent electric vehicle to driving styles. A cyber-physical system (CPS)-based framework is proposed for codesign optimization of the plant and controller parameters for an automated electric vehicle, in view of vehicle's dynamic performance, drivability, and energy along with different driving styles. System description, requirements, constraints, optimization objectives, and methodology are investigated. Driving style recognition algorithm is developed using unsupervised machine learning and validated via vehicle experiments. Adaptive control algorithms are designed for three driving styles with different protocol selections. Performance exploration method is presented. Parameter optimizations are implemented based on the defined objective functions. Test results show that an automated vehicle with optimized plant and controller can perform its tasks well under aggressive, moderate, and conservative driving styles, further improving the overall performance. The results validate the feasibility and effectiveness of the proposed CPS-based codesign optimization approach.
213 citations
TL;DR: An adaptive neural network (NN) control scheme is proposed for a quarter-car model, which is the active suspension system (ASS) with the time-varying vertical displacement and speed constraints and unknown mass of car body and it can prove the stability of the closed-loop system.
Abstract: In this paper, an adaptive neural network (NN) control scheme is proposed for a quarter-car model, which is the active suspension system (ASS) with the time-varying vertical displacement and speed constraints and unknown mass of car body. The NNs are used to approximate the unknown mass of car body. It is commonly known that the stability and security of the ASSs will be weakened when the constraints are violated. Thus, the control problem of the time-varying vertical displacement and speed constraints for the quarter-car ASSs is a very important task because of the demand of the handing safety. The time-varying barrier Lyapunov functions are used to guarantee the constraints of the vertical displacement not violated, and it can prove the stability of the closed-loop system. Finally, a simulation example for the ASSs is employed to show the feasibility and rationality of the proposed approach.
188 citations
TL;DR: This paper investigates a fault-tolerant control of the hypersonic flight vehicle using back-stepping and composite learning and results show that the controller obtains good system tracking performance in the presence of AOA constraint and actuator faults.
Abstract: This paper investigates a fault-tolerant control of the hypersonic flight vehicle using back-stepping and composite learning. With consideration of angle of attack (AOA) constraint caused by scramjet, the control laws are designed based on barrier Lyapunov function. To deal with the unknown actuator faults, a robust adaptive allocation law is proposed to provide the compensation. Meanwhile, to obtain good system uncertainty approximation, the composite learning is proposed for the update of neural weights by constructing the serial–parallel estimation model to obtain the prediction error which can dynamically indicate how the intelligent approximation is working. Simulation results show that the controller obtains good system tracking performance in the presence of AOA constraint and actuator faults.
173 citations
20 May 2019
TL;DR: A novel deep-learning-based robust nonlinear controller (Neural-Lander) that improves control performance of a quadrotor during landing and is the first DNN-based nonlinear feedback controller with stability guarantees that can utilize arbitrarily large neural nets.
Abstract: Precise near-ground trajectory control is difficult for multi-rotor drones, due to the complex aerodynamic effects caused by interactions between multi-rotor airflow and the environment. Conventional control methods often fail to properly account for these complex effects and fall short in accomplishing smooth landing. In this paper, we present a novel deep-learning-based robust nonlinear controller (Neural-Lander) that improves control performance of a quadrotor during landing. Our approach combines a nominal dynamics model with a Deep Neural Network (DNN) that learns high-order interactions. We apply spectral normalization (SN) to constrain the Lipschitz constant of the DNN. Leveraging this Lipschitz property, we design a nonlinear feedback linearization controller using the learned model and prove system stability with disturbance rejection. To the best of our knowledge, this is the first DNN-based nonlinear feedback controller with stability guarantees that can utilize arbitrarily large neural nets. Experimental results demonstrate that the proposed controller significantly outperforms a Baseline Nonlinear Tracking Controller in both landing and cross-table trajectory tracking cases. We also empirically show that the DNN generalizes well to unseen data outside the training domain.
164 citations
TL;DR: This paper is concerned with reliable fuzzy tracking control for a near-space hypersonic vehicle (NSHV) subject to aperiodic measurement information and stochastic actuator failures.
Abstract: This paper is concerned with reliable fuzzy tracking control for a near-space hypersonic vehicle (NSHV) subject to aperiodic measurement information and stochastic actuator failures. The NSHV dynamics is approximated by the Takagi–Sugeno fuzzy models and the stochastic failures are characterized by a Markov chain. Different with the existing tracking results on NSHV, only the aperiodic sampling measurements are available during system operation. To realize the tracking objective, a reliable fuzzy sampled-data tracking control strategy is presented. An appropriate time-dependent Lyapunov function is constructed to fully capture the real sampling pattern. The sampling-interval-dependent mean square exponential stability criterion with disturbance attenuation is then established. The solution of the tracking controller gains can be obtained by solving an optimization problem. Finally, the simulation studies on NSHV dynamics in the entry phase are performed to verify the validity of the developed fuzzy tracking control strategy.
157 citations
TL;DR: The proposed MPC algorithm has been proved by simulation to have the ability to avoid obstacles and mitigate the crash if collision is inevitable.
Abstract: A motion planning method for autonomous vehicles confronting emergency situations where collision is inevitable, generating a path to mitigate the crash as much as possible, is proposed in this paper. The Model predictive control (MPC) algorithm is adopted here for motion planning. If avoidance is impossible for the model predictive motion planning system, the potential crash severity, and artificial potential field are filled into the controller objective to achieve general obstacle avoidance and the lowest crash severity. Furthermore, the vehicle dynamic is also considered as an optimal control problem. Based on the analysis mentioned earlier, the model predictive controller can optimize the command following, obstacle avoidance, vehicle dynamics, road regulation, and mitigate the inevitable crash based on the predicted values. The proposed MPC algorithm has been proved by simulation to have the ability to avoid obstacles and mitigate the crash if collision is inevitable.
151 citations
TL;DR: Simulations illustrate the effectiveness of the proposed bounded controller for tracking a moving target, which is designed based on the neural estimation model and a saturated function and bounded with the bounds known as a priori.
Abstract: This paper is concerned with the target tracking of underactuated autonomous surface vehicles with unknown dynamics and limited control torques. The velocity of the target is unknown, and only the measurements of line-of-sight range and angle are obtained. First, a kinematic control law is designed based on an extended state observer, which is utilized to estimate the uncertain target dynamics due to the unknown velocities. Next, an estimation model based on a single-hidden-layer neural network is developed to approximate the unknown follower dynamics induced by uncertain model parameters, unmodeled dynamics, and environmental disturbances. A bounded control law is designed based on the neural estimation model and a saturated function. The salient feature of the proposed controller is twofold. First, only the measured line-of-sight range and angle are used, and the velocity information of the target is not required. Second, the control torques are bounded with the bounds known as a priori . The input-to-state stability of the closed-loop system is analyzed via cascade theory. Simulations illustrate the effectiveness of the proposed bounded controller for tracking a moving target.
141 citations
TL;DR: A control algorithm combining artificial potential field approach with model predictive control (MPC), and using the optimizer of the MPC controller to replace the gradient-descending method in the traditional APF approach is presented, which can accomplish both path planning and motion control synchronously.
Abstract: Cooperative driving systems may increase the utilization of road infrastructure resources through coordinated control and platooning of individual vehicles with the potential of enhancing both traffic safety and efficiency. Vehicle cooperative driving is essentially a hybrid system that is a combination of discrete events, i.e., the transition of discrete cooperative maneuvering modes, such as vehicle merging and platoon splitting, as well as continuous vehicle dynamics. In this paper, a novel hybrid system consisting of the discrete cooperative maneuver switch and the continuous vehicle motion control is introduced into a multi-vehicle cooperative control system with a distributed control structure, leading each automated vehicle to conduct path planning and motion control separately. The primary novelty of this paper lies in that it presents a control algorithm combining artificial potential field (APF) approach with model predictive control (MPC), and using the optimizer of the MPC controller to replace the gradient-descending method in the traditional APF approach. Such a method can accomplish both path planning and motion control synchronously. Second, based on hybrid automata, a cooperative maneuver switching model consisting of a system state set and a discrete maneuver transition rule is established for two discrete maneuvers in the cooperative driving system, i.e., single-vehicle cruising and multiple-vehicle platooning. Simulations in several typical traffic scenarios demonstrate the effectiveness of the proposed method.
125 citations
TL;DR: The problem of steering control is investigated for vehicle path tracking in the presence of parametric uncertainties and nonlinearities, and the effectiveness of the proposed fuzzy observer-based output feedback controller is demonstrated in Carsim/Matlab joint simulation environment.
Abstract: In this paper, the problem of steering control is investigated for vehicle path tracking in the presence of parametric uncertainties and nonlinearities In practice, the vehicle mass varies due to the number of passengers or amount of payload, while the vehicle velocity also changes during normal cruising, which significantly influences vehicle dynamics Moreover, the vehicle dynamics are strongly nonlinear caused by the tire/road forces under different road surface conditions With fuzzy modeling method, the original nonlinear path tracking system with parameter variations is first formulated as a T–S fuzzy model with additive norm-bounded uncertainties, and then an approach to the fuzzy observer-based output feedback steering control for vehicle dynamics is proposed under a fuzzy Lyapunov function framework By employing matrix inequality convexifying techniques, a sufficient condition is developed in the form of linear matrix inequalities such that the closed-loop path tracking error system is asymptotically stable with a guaranteed $\mathcal {H}_{\infty }$ level Finally, the effectiveness of the proposed fuzzy observer-based output feedback controller is demonstrated in Carsim/Matlab joint simulation environment, via which the advantage of a T–S fuzzy observer-based output controller over the closed-loop driver model embedded in Carsim is also shown with parametric uncertainties and nonlinearities
120 citations
TL;DR: A robust AGV path following control strategy that is based on nonsingular terminal sliding mode (NTSM) and active disturbance rejection control (ADRC) and the nonlinear error feedback control law is designed by combining the NTSM and exponential approximation law.
Abstract: Due to the strong nonlinearity, coupling characteristics, external disturbance, and complex driving conditions, it is difficult to establish an accurate mathematical model for the autonomous ground vehicle (AGV). This requires the AGV path following controller to have strong robustness. In this paper, a robust AGV path following control strategy that is based on nonsingular terminal sliding mode (NTSM) and active disturbance rejection control (ADRC) is presented. First, the complex path following problem is simplified to a simple yaw angle tracking problem by constructing a desired yaw angle function that satisfies that the displacement deviation of AGV converges to zero when the actual yaw angle approaches the desired yaw angle. Second, an NTSM-ADRC controller is designed for the system, which uses the extended state observer to estimate and compensate the unmodeled dynamics and unknown external perturbations of the system in real time. In order to improve response characteristics of the controller, the nonlinear error feedback control law is designed by combining the NTSM and exponential approximation law. In contrast to the existing work, the improved controller can use the simple two-degree-of-freedom linear vehicle dynamic model to provide good performance in a range of driving conditions. Finally, the CarSim–Simulink simulation results of typical conditions show that the proposed control strategy can make the AGV follow the reference path quickly and accurately while ensuring the stability of the vehicle and has strong robustness.
TL;DR: Using the Lyapunov analysis, this work is able to prove that the closed-loop system has an external dynamics that is globally exponentially stable and an internal dynamics that has ultimately bounded states, both for the trajectory tracking and the path following control problems.
Abstract: In this paper, we present a control strategy for trajectory tracking and path following of generic paths for underactuated marine vehicles. Our work is inspired and motivated by previous works on ground vehicles. In particular, we extend the definition of the hand position point, introduced for ground vehicles, to autonomous surface vehicles and autonomous underwater vehicles, and then use the hand position point as output for a control strategy based on the input–output feedback linearization method. The presented strategy is able to deal with external disturbances affecting the vehicle, e.g., constant and irrotational ocean currents. Using the Lyapunov analysis, we are able to prove that the closed-loop system has an external dynamics that is globally exponentially stable and an internal dynamics that has ultimately bounded states, both for the trajectory tracking and the path following control problems. Finally, we present a simulation case study and experimental results in order to validate the theoretical results.
TL;DR: The RBFNN and composite nonlinear feedback (CNF) based ISMC is developed to achieve the yaw stabilization and enhance the transient tracking performance considering the input saturation of the front steering angle and the overall stability is proved with Lyapunov function.
Abstract: This paper investigates the path-tracking control issue for autonomous ground vehicles with the integral sliding mode control (ISMC) considering the transient performance improvement. The path-tracking control is converted into the yaw stabilization problem, where the sideslip-angle compensation is adopted to reduce the steady-state errors, and then the yaw-rate reference is generated for the path-tracking purpose. The lateral velocity and roll angle are estimated with the measurement of the yaw rate and roll rate. Three contributions have been made in this paper: first, to enhance the estimation accuracy for the vehicle states in the presence of the parametric uncertainties caused by the lateral and roll dynamics, a robust extended Kalman filter is proposed based on the minimum model error algorithm; second, an improved adaptive radial basis function neural network (RBFNN) considering the approximation error adaptation is developed to compensate for the uncertainties caused by the vertical motion; third, the RBFNN and composite nonlinear feedback (CNF) based ISMC is developed to achieve the yaw stabilization and enhance the transient tracking performance considering the input saturation of the front steering angle. The overall stability is proved with Lyapunov function. Finally, the superiority of the developed control strategy is verified by comparing with the traditional CNF with high-fidelity CarSim-MATLAB simulations.
TL;DR: Through the CarSim-Matlab/Simulink co-simulations, the results show that this improved Model Predictive Control controller presents better tracking performance than the latter ones considering both tracking accuracy and steering smoothness.
Abstract: In this paper, an improved Model Predictive Control (MPC) controller based on fuzzy adaptive weight control is proposed to solve the problem of autonomous vehicle in the process of path tracking. The controller not only ensures the tracking accuracy, but also considers the vehicle dynamic stability in the process of tracking, i.e., the vehicle dynamics model is used as the controller model. Moreover, the problem of driving comfort caused by the application of classical MPC controller when the vehicle is deviated from the target path is solved. This controller is mainly realized by adaptively improving the weight of the cost function in the classical MPC through the fuzzy adaptive control algorithm. A comparative study which compares the proposed controller with the pure-pursuit controller and the classical MPC controller is made: through the CarSim-Matlab/Simulink co-simulations, the results show that this controller presents better tracking performance than the latter ones considering both tracking accuracy and steering smoothness.
14 Nov 2019
TL;DR: This paper extends existing studies on distributed platoon control to more generic topologies with complex eigenvalues, including both internal stability analysis and linear controller synthesis, and proposes a Riccati inequality based algorithm to calculate the feasible static control gain.
Abstract: The platooning of autonomous vehicles can significantly benefit road traffic. Most previous studies on platoon control have only focused on specific communication topologies, especially those with real eigenvalues. This paper extends existing studies on distributed platoon control to more generic topologies with complex eigenvalues, including both internal stability analysis and linear controller synthesis. Linear platoon dynamics are derived using an inverse vehicle model compensation, and graph theory is employed to model the communication topology, leading to an integrated high-dimension linear model of the closed-loop platoon dynamics. Using the similarity transformation, a sufficient and necessary condition is derived for the internal stability, which is completely defined in real number field. Then, we propose a Riccati inequality based algorithm to calculate the feasible static control gain. Further, disturbance propagation is formulated as an $\text {H}_{\infty }$ performance, and the upper bound of spacing errors is explicitly derived using Lyapunov analysis. Numerical simulations with a nonlinear vehicle model validate the effectiveness of the proposed methods.
TL;DR: A nonlinear model predictive control (NMPC) method integrating active front steering and an additional yaw moment is proposed, which adopts the tire sideslip angle to express vehicle lateral stability, and addresses the actuator and security constraints and the nonlinear properties of the tire-road force effectively.
Abstract: The rapid development of intelligent vehicles has paved the way for active chassis lateral stability, which is a novel issue and critical to vehicle stability and handling performance. To obtain active chassis lateral stability for intelligent vehicles, a nonlinear model predictive control (NMPC) method integrating active front steering and an additional yaw moment is proposed. It adopts the tire sideslip angle to express vehicle lateral stability, and addresses the actuator and security constraints and the nonlinear properties of the tire-road force effectively. Moreover, the hardware implementation, based on the field programmable gate array (FPGA), is presented to satisfy miniaturization and to discuss the computational efficiency of the proposed NMPC method. To verify the effectiveness of the presented NMPC method, offline simulations comparing the NMPC method with the direct yaw moment control (DYC) method under various running conditions and a real-time implementation experiment are carried out. The results indicate that the proposed NMPC method controls better than the DYC-based method. In addition, the presented NMPC method exhibits good robustness when the longitudinal velocity and tire-road friction coefficient vary within a suitable range. Moreover, the computational time of the proposed NMPC controller, implemented using the FPGA, is only 4.994 ms during one sampling period, which can satisfy the real-time requirement of active chassis lateral stability control.
TL;DR: A distributed integral-sliding-mode (ISM) control strategy for cooperative braking control of a connected vehicle platoon with a focus on the car-following interactions between vehicles is proposed and verified with respect to the position, velocity, deceleration, and spacing error profiles.
Abstract: This paper proposes a distributed integral-sliding-mode (ISM) control strategy for cooperative braking control of a connected vehicle platoon with a focus on the car-following interactions between vehicles. In particular, a linear controller considering the position and velocity of the lead vehicle as well as the braking force is proposed for the leader, while a constant-time-headway-policy-based ISM controller incorporating the car-following interactions, the spacing error, velocity difference, and external disturbances is developed for the followers. In addition, the convergence for the ISM controller is rigorously analyzed using the Lyapunov technique. Furthermore, the string stability of the platoon is analyzed using the transfer function method. Finally, extensive analyses are conducted using numerical and field experiments. Results verify the effectiveness of the proposed control strategy with respect to the position, velocity, deceleration, and spacing error profiles.
TL;DR: In this article, the authors proposed a time-optimal velocity planning method for guaranteeing comfort criteria when an explicit reference path is given, and the overall controller and planning method were verified using real-time, software-in-the-loop (SIL) environments for a realtime vehicle dynamics simulation; the performance was then compared with a typical planning approach.
Abstract: The convergence of mechanical, electrical, and advanced ICT technologies, driven by artificial intelligence and 5G vehicle-to-everything (5G-V2X) connectivity, will help to develop high-performance autonomous driving vehicles and services that are usable and convenient for self-driving passengers. Despite widespread research on self-driving, user acceptance remains an essential part of successful market penetration; this forms the motivation behind studies on human factors associated with autonomous shuttle services. We address this by providing a comfortable driving experience while not compromising safety. We focus on the accelerations and jerks of vehicles to reduce the risk of motion sickness and to improve the driving experience for passengers. Furthermore, this study proposes a time-optimal velocity planning method for guaranteeing comfort criteria when an explicit reference path is given. The overall controller and planning method were verified using real-time, software-in-the-loop (SIL) environments for a real-time vehicle dynamics simulation; the performance was then compared with a typical planning approach. The proposed optimized planning shows a relatively better performance and enables a comfortable passenger experience in a self-driving shuttle bus according to the recommended criteria.
TL;DR: The paper addresses the leader tracking problem for a platoon of connected autonomous vehicles in the presence of both homogeneous time-varying parameter uncertainties and vehicle-to-vehicle time-Varying communication delay with a novel distributed robust proportional-integral-derivative control framework.
Abstract: The paper addresses the leader tracking problem for a platoon of connected autonomous vehicles in the presence of both homogeneous time-varying parameter uncertainties and vehicle-to-vehicle time-varying communication delay. To this aim, leveraging the multiagent systems (MAS) framework, a novel distributed robust proportional-integral-derivative control is proposed. The stability of the cohesive formation is analytically proved with a Lyapunov–Krasovskii approach by exploiting the descriptor transformation for time-delayed systems of neutral type. The delay-dependent robust stability conditions are expressed as a set of linear matrix inequalities allowing the proper tuning of the proportional, integral, and derivative actions implemented on each of the vehicles within the fleet. Extensive simulation analysis in different driving scenarios confirms the effectiveness of the theoretical derivation.
TL;DR: A model predictive control (MPC) path-tracking controller with switched tracking error is presented, which reduces the lateral tracking deviation and maintains vehicle stability for both normal and high-speed conditions.
Abstract: Autonomous vehicle path tracking accuracy and vehicle stability can hardly be accomplished by one fixed control frame in various conditions due to the changing vehicle dynamics. This paper presents a model predictive control (MPC) path-tracking controller with switched tracking error, which reduces the lateral tracking deviation and maintains vehicle stability for both normal and high-speed conditions. The design begins by comparing the performance of three MPC controllers with different tracking error. The analyzing results indicate that in the steady-state condition the controller with the velocity heading deviation as the tracking error significantly improves the tracking accuracy. Meanwhile, in the transient condition, by substituting the steady-state sideslip for real-time sideslip to compute the velocity heading deviation, the tracking overshoot can be reduced. To combine the strengths of these two methods, an MPC controller with switched tracking error is designed to improve the performance in both steady-state and transient conditions. The regime condition of a vehicle maneuver and the switching instant are determined by a fuzzy-logic-based condition classifier. Both normal and aggressive driving scenarios with the vehicle lateral and longitudinal acceleration combination of 5 m/s2 and 8 m/s2 are designed to test the proposed controller through CarSim-Simulink platform. The simulation results show the improved performance of the MPC controller with switched tracking error both in tracking accuracy and vehicle stability in both scenarios.
TL;DR: A new control structure is proposed that uses an estimate of dynamic parameters to transform the heterogeneous CACC problem into the regulation problem of error dynamics for each vehicle in the platoon.
Abstract: Cooperative adaptive cruise control (CACC), as an extension of adaptive cruise control, connects multiple vehicles in a platoon via wireless communication. In practice, different vehicles may have different dynamic parameters and their exact values are unknown/uncertain to designers. In this brief, we propose a new control structure that uses an estimate of dynamic parameters to transform the heterogeneous CACC problem into the regulation problem of error dynamics for each vehicle in the platoon. An adaptive optimal control is proposed to learn the optimal feedback based on online data. The position transfer function between adjacent vehicles is further analyzed in the frequency domain. By sum of squares programming, the minimum headway values that ensure the vehicle string stability are found. Experiments on numerical and complex systems validate our method.
TL;DR: In this article, a hydraulic interconnected suspension based on the energy regenerative shock absorbers (HIS-HESA) is proposed to improve ride comfort, road holding ability and energy recovery ability simultaneously.
01 Nov 2019
TL;DR: The ability to perform vehicle-in-the-loop experiments with photorealistic exteroceptive sensor simulation facilitates novel research directions involving, e.g., fast and agile autonomous flight in obstacle-rich environments, safe human interaction, and flexible sensor selection.
Abstract: FlightGoggles is a photorealistic sensor simulator for perception-driven robotic vehicles. The key contributions of FlightGoggles are twofold. First, FlightGoggles provides photorealistic exteroceptive sensor simulation using graphics assets generated with photogrammetry. Second, it provides the ability to combine (i) synthetic exteroceptive measurements generated in silico in real time and (ii) vehicle dynamics and proprioceptive measurements generated in motio by vehicle(s) in flight in a motion-capture facility. FlightGoggles is capable of simulating a virtual-reality environment around autonomous vehicle(s) in flight. While a vehicle is in flight in the Flight-Goggles virtual reality environment, exteroceptive sensors are rendered synthetically in real time while all complex dynamics are generated organically through natural interactions of the vehicle. The FlightGoggles framework allows for researchers to accelerate development by circumventing the need to estimate complex and hard-to-model interactions such as aerodynamics, motor mechanics, battery electrochemistry, and behavior of other agents. The ability to perform vehicle-in-the-loop experiments with photorealistic exteroceptive sensor simulation facilitates novel research directions involving, e.g., fast and agile autonomous flight in obstacle-rich environments, safe human interaction, and flexible sensor selection. FlightGoggles has been utilized as the main test for selecting nine teams that will advance in the AlphaPilot autonomous drone racing challenge. We survey approaches and results from the top AlphaPilot teams, which may be of independent interest. FlightGoggles is distributed as open-source software along with the photorealistic graphics assets for several simulation environments, under the MIT license at http://flightgoggles.mit.edu.
TL;DR: The FSRC objective is divided into two subtasks, i.e., reaching trajectory guidance and tracking controller synthesis with underactuation and disturbances, and the AUSV is regulated to the desired full-state waypoint (FWP).
Abstract: In this paper, a challenging problem of full-state regulation control (FSRC) for an asymmetric underactuated surface vehicle (AUSV) suffering from disturbances is solved. The FSRC objective is divided into two subtasks, i.e., reaching trajectory (RT) guidance and tracking controller synthesis with underactuation and disturbances. The RT guidance is achieved by devising a circular orbit (CO) accurately passing through the desired full-state waypoint (FWP). Using a series of coordinate transformations, tracking error dynamics are shaped in a translation-rotation cascade form with respect to the CO-center frame. Using finite-time approach, lumped disturbances are accurately estimated by exact observers, which facilitate synthesizing surge and yaw controllers. By creating a new coordinate, translation subsystem is converted to a lower-triangular form. Combining with backstepping technique, cascade analysis and Lypunov approach, translation and rotation controllers are derived systematically, and render the entire closed-loop FSRC system globally asymptotically stable. Hence, the AUSV is regulated to the desired FWP. Simulation studies on a benchmark AUSV are conducted to demonstrate remarkable performance.
TL;DR: This paper addresses a trajectory-tracking control problem for mobile robots by combining tube-based model predictive control (MPC) in handling kinematic constraints and adaptive control in handling dynamic constraints.
Abstract: This paper addresses a trajectory-tracking control problem for mobile robots by combining tube-based model predictive control (MPC) in handling kinematic constraints and adaptive control in handling dynamic constraints. In order to handle kinematic constraints, the tube-based MPC scheme is introduced, which includes the state feedback controller to suppress the external disturbance in the velocity level. The tube-based MPC is transformed to a constrained quadratic programming (QP) problem, and then the QP problem can be efficiently solved by a primal-dual neural network over a finite receding horizon so as to obtain the optimal control velocity. Besides, an adaptive controller employing the neural network technology is proposed to acquire the approximation of the uncertain robotic dynamics. Moreover, an auxiliary control is developed in order to deal with actuator saturation, and a disturbance observer is designed to reject the external disturbance online in the dynamic level. Subsequently, through Lyapunov function synthesis, the stability of the closed-loop system have been guaranteed. Finally, in order to verify the effectiveness, the experimental studies are carried out using an actual mobile robot.
TL;DR: In this paper, real-time estimates of the vehicle dynamic states and tire-road contact parameters are provided for automotive chassis control systems, where feedback control structures employ a feedback control structure.
Abstract: Most modern day automotive chassis control systems employ a feedback control structure. Therefore, real-time estimates of the vehicle dynamic states and tire-road contact parameters are invaluable ...
TL;DR: A non-linear model-based observer for combined estimation of motion states and tyre cornering stiffness is presented, based on common onboard sensors, that is a lateral acceleration and yaw rate sensor, and it works during normal vehicle manoeuvering.
01 Mar 2019
TL;DR: How a longitudinal controller based on distributed consensus can guarantee stability and performance in regime platoon operations, and be at the hearth of maneuvering protocols and algorithms, as it remains stable in face of changes of platoon topology and control gains is shown.
Abstract: Cooperative driving is an essential component of future intelligent road systems. It promises greater safety, reducing accidents due to drivers distraction, improved infrastructure utilization, and fuel consumption reduction with platooning applications. Proper platoon management requires inter-vehicular communication (IVC), longitudinal control and lateral control for stability and safety, and proper application protocols and algorithms to manage platoons and perform coordinated maneuvers. This paper shows, how a longitudinal controller based on distributed consensus can, at the same time, guarantee stability and performance in regime platoon operations, and be at the hearth of maneuvering protocols and algorithms, as it remains stable in face of changes of platoon topology and control gains. The adoption of a single control algorithm for two fundamental tasks greatly simplify the overall design of the system and improves stability and safety as it is not required to switch between different controllers during platoon operation. The theoretical properties are proven in the first part of the paper. The second part of the paper is devoted to its implementation in a state-of-the-art mobility and IVC simulator, which is used for an extensive experimental campaign showing the dynamic properties of the system and its performance in a set of typical platoon maneuvers as join, leave, and inclusion of a vehicle in the middle of the platoon. All simulations include realistic details of the vehicle dynamics (mass, dimensions, power train dynamics) as well as extremely detailed modeling of the communication network, from IEEE 802.11p protocol details, to collisions, packet errors, path loss and fading on the channel, and source-destination-based delays.
28 Aug 2019
TL;DR: A tube-based robust Model Predictive Control approach is introduced, designed in order to guarantee certain comfort standards for a wide range of velocities, with guaranteed stability.
Abstract: This paper presents a path following application for vehicles based on a simple linear and time invariant single-track model, which is calculated based on a constant nominal longitudinal speed. In order to consider the differences in vehicle dynamics between the real vehicle and this constant nominal model, a tube-based robust Model Predictive Control (MPC) approach is introduced. The proposed control algorithm is designed in order to guarantee proper path tracking, not only considering lateral error, but also orientation error to the target trajectory. Additionally, strict constraints are considered in the control signal and the lateral path following error. The control approach is designed in order to guarantee certain comfort standards for a wide range of velocities, with guaranteed stability.